Knowledge check

Quiz

In Progress

Knowledge check

Evaluate your understanding of this unit by completing the knowledge check below.

Action item: Knowledge check

Work through each question. Correct answers and feedback mirror the Multiverse assessment.

1. Which statement best describes the goal of model training in machine learning?

A. To memorise all patterns in the training data for maximum accuracy.B. To identify and store all training examples for use in predictions.C. To adjust model parameters to minimize the loss function and improve predictions.D. To reduce computational costs by skipping gradient updates.

2. In gradient descent, what does the learning rate control?

A. The direction in which the gradient points.B. The number of iterations required to reach convergence.C. The size of the step taken toward the minimum of the loss function.D. The total number of features used in training.

3. Which of the following best describes a limitation of vanilla gradient descent?

A. It updates weights too frequently for large datasets.B. It struggles with slow convergence and can get stuck in local minima.C. It automatically adjusts learning rates for each parameter.D. It is only effective for non-linear models.

4. Which optimizer combines the advantages of momentum and RMSprop?

A. AdagradB. SGD with Nesterov acceleration.C. AdadeltaD. Adam (Adaptive Moment Estimation)

5. Which situation would most likely benefit from using RMSprop?

A. A large, noisy dataset where gradients vary significantly between batches.B. A small, clean dataset with stable gradients.C. A dataset with sparse features and rare signals.D. A small dataset requiring strong feature selection.

6. What is the main purpose of L1 regularisation (Lasso regression)?

A. To reduce the influence of correlated features without removing them.B. To eliminate irrelevant features by forcing their coefficients to zero.C. To maintain all features but shrink their coefficients.D. To reduce computation time during gradient updates.

7. What happens when the regularisation strength (λ) is set too high?

A. The model converges faster to the global minimum.B. The model perfectly fits both training and test data.C. The model ignores the bias term.D. The model may underfit the data.

8. Which statement best describes dropout in neural networks?

A. It permanently removes neurons from the network to simplify architecture.B. It increases the learning rate for underperforming neurons.C. It randomly deactivates neurons during training to prevent over-reliance on specific pathways.D. It duplicates neurons to create redundant layers.

9. Which regularisation technique is most useful when your dataset has many correlated features?

A. L2 (Ridge)B. L1 (Lasso)C. Early stoppingD. Dropout

10. If your training loss is much lower than your validation loss, what’s the most likely cause?

A. Underfitting — the model hasn’t learned enough from the training data.B. Data leakage — the test data was used during training.C. Overfitting — the model has learned noise from the training data.D. Poor optimiser selection — the model didn’t converge.

In progress